Learning outcome
1.1

1.1 Demonstrate a coherent understanding of the mathematical sciences.
2.1

2.1 Exhibit depth and breadth of knowledge in the mathematical sciences.
3.1

3.1 Investigating and solving problems using mathematical and statistical methods.
4.1

4.1 Communicate mathematical and statistical information, arguments, or results for a range of purposes using a variety of means.
5.1

5.1 Demonstrate personal, professional and social responsibility.
A1

Build regression models for real life applications.
A2

Apply regression models to predict future events and conditions.
K1

Describe relationship between dependent and independent variables using appropriate linear regression models.
K2

Describe relationships using time series regression models.
K3

List regression assumptions, and evaluate model appropriateness from these assumptions.
K4

Recognise importance of regression models for predictions.
S1

Apply available software such as SPSS and MINITAB to develop regression models.
S2

Build regression models using iterative model selection procedure such as stepwise regression and backward elimination.
S3

Perform appropriate diagnostics for detecting outlying and influential observations prior to model development.
S4

Perform appropriate hypothesis tests to determine the significance of independent variables in a regression model.
S5

Build appropriate time series regression models.
S6

Use linear regression and time series models for predictions.
S7

Present clear, orderly and informative statistical summaries and technical reports.